English

Reflective Gaussian Splatting

Computer Vision and Pattern Recognition 2025-02-04 v2

Abstract

Novel view synthesis has experienced significant advancements owing to increasingly capable NeRF- and 3DGS-based methods. However, reflective object reconstruction remains challenging, lacking a proper solution to achieve real-time, high-quality rendering while accommodating inter-reflection. To fill this gap, we introduce a Reflective Gaussian splatting (Ref-Gaussian) framework characterized with two components: (I) Physically based deferred rendering that empowers the rendering equation with pixel-level material properties via formulating split-sum approximation; (II) Gaussian-grounded inter-reflection that realizes the desired inter-reflection function within a Gaussian splatting paradigm for the first time. To enhance geometry modeling, we further introduce material-aware normal propagation and an initial per-Gaussian shading stage, along with 2D Gaussian primitives. Extensive experiments on standard datasets demonstrate that Ref-Gaussian surpasses existing approaches in terms of quantitative metrics, visual quality, and compute efficiency. Further, we show that our method serves as a unified solution for both reflective and non-reflective scenes, going beyond the previous alternatives focusing on only reflective scenes. Also, we illustrate that Ref-Gaussian supports more applications such as relighting and editing.

Keywords

Cite

@article{arxiv.2412.19282,
  title  = {Reflective Gaussian Splatting},
  author = {Yuxuan Yao and Zixuan Zeng and Chun Gu and Xiatian Zhu and Li Zhang},
  journal= {arXiv preprint arXiv:2412.19282},
  year   = {2025}
}

Comments

Accepted for ICLR 2025

R2 v1 2026-06-28T20:49:19.966Z